MULTIBIO

A current problem in biological and medical research is how to use existing biological knowledge and heterogeneous experimental data in making inferences on new data. We study new computational methods and theory for the fusion of multiple biological information sources with partially-relevant background data from existing and new databanks. We argue that using the available public or private background information from hundreds of different situations or conditions, it is possible to both complement the existing scarce data and to focus the analysis on relevant variables.

The project complements the task-dependent bioinformatics methods, which are naturally required in all biological and medical research problems as well, with methods that address a key underlying statistical limitation in current studies using high-throughput measurement techniques: large p, small n. It is very hard to make trustworthy computational models or statistically significant diagnoses based on only few samples (small n) when the number of studied genes or metabolites (p) is large.

This project is partially based on a previous project in the SYSBIO programme of the Academy of Finland.

Research Groups

Prof. Samuel Kaski's Statistical Machine Learning and Bioinformatics research group at the Helsinki University of Technology
Prof. Sakari Knuutila's research group at the Laboratory of Cytomolecular Genetics
Prof. Jaakko Kangasjärvi's Plant Stress research group at the Finnish Centre of Excellence in Plant Signal Research
Dr. Alvis Brazma's functional genomics group at the European Bioinformatics Institute
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